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OPTIMAL PID CONTROLLER DESIGN FOR TRAJECTORY TRACKING OF A DODECAROTOR UAV BASED ON GREY WOLF OPTIMIZER

Year 2023, Volume: 11 Issue: 1, 10 - 20, 01.03.2023
https://doi.org/10.36306/konjes.1172687

Abstract

In this study, we aimed to find optimal PD controller gains to control orientation and position of a Dodecarotor UAV with minimum trajectory error. In this context, a cascaded PD controller approach which has velocity feedback in the inner loop and position feedback in the outer loop was adopted for each state (roll, pitch, yaw, altitude) in the flight control of the UAV. Subsequently, a fitness function was defined based on the system's time domain response and trajectory tracking error for each state, except the yaw angle, which is non-dominant in terms of trajectory tracking performance. Grey Wolf Optimizer (GWO) was used to obtain PD gains by minimizing the defined fitness function. At the same time, Particle Swarm Optimizer was used in order to benchmark the obtained results from GWO and to avoid a shallow solution space. The obtained PD controller parameters as a result of the optimization study of both algorithms were implemented to the system and the results were compared with each other. Finally, the gains that provided the best results for both algorithms were compared with each other and the results were discussed in terms of the time domain results and the actuator input smoothness. It has been observed that the GWO optimized controller provides a 40-46% improvement over PSO in all four different mass UAVs in terms of reducing axis position errors.

Supporting Institution

Erciyes University SRU (BAP) Unit

Project Number

FBA-2017-7393

References

  • [1] D. Kotarski, Z. Benic, and M. Krznar, “Control Design for Unmanned Aerial Vehicles with Four Rotors,” Interdiscip. Descr. Complex Syst., vol. 14, no. 2, pp. 236–245, 2016.
  • [2] N. Hadi and A. Ramz, “Tuning of PID Controllers for Quadcopter System using Hybrid Memory based Gravitational Search Algorithm – Particle Swarm Optimization,” Int. J. Comput. Appl., vol. 172, no. 4, pp. 9–18, 2017.
  • [3] M. Akhil, M. Krishna Anand, A. Sreekumar, and P. Hithesan, “Simulation of the mathematical model of a quad rotor control system using Matlab Simulink,” Appl. Mech. Mater., vol. 110–116, no. October 2011, pp. 2577–2584, 2012.
  • [4] M. Ayyıldız and K. Çetinkaya, “Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator,” Neural Comput. Appl., vol. 27, no. 4, pp. 825–836, 2016.
  • [5] M. B. Çetinkaya, H. Duran, and T. Hanne, “Performance Comparison of Most Recently Proposed Evolutionary, Swarm Intelligence, and Physics-Based Metaheuristic Algorithms for Retinal Vessel Segmentation,” Math. Probl. Eng., vol. 2022, 2022.
  • [6] M. Mahi, O. K. Baykan, and H. Kodaz, “A new approach based on particle swarm optimization algorithm for solving data allocation problem,” Appl. Soft Comput. J., vol. 62, pp. 571–578, 2018.
  • [7] M. A. Sen and M. Kalyoncu, “Grey wolf optimizer based tuning of a hybrid LQR-PID controller for foot trajectory control of a quadruped robot,” Gazi Univ. J. Sci., vol. 32, no. 2, pp. 674–684, 2019.
  • [8] M. Moussid, A. Sayouti, and H. Medromi, “Dynamic Modeling and Control of a HexaRotor using Linear and Nonlinear Methods,” Int. J. Appl. Inf. Syst., vol. 9, no. 5, pp. 9–17, 2015.
  • [9] S. Li, Y. Wang, J. Tan, and Y. Zheng, “Adaptive RBFNNs/integral sliding mode control for a quadrotor aircraft,” Neurocomputing, vol. 216, pp. 126–134, 2016.
  • [10] Y.-R. Tang, X. Xiao, and Y. Li, “Nonlinear dynamic modeling and hybrid control design with dynamic compensator for a small-scale UAV quadrotor,” Measurement, vol. 109, pp. 51–64, Oct. 2017.
  • [11] Ş. Yıldırım, N. Çabuk, and V. Bakırcıoğlu, “Comparison of Flight Performances of Unmanned Air Vehicle with Six Rotors and Eight Rotors Under Different Disturbance Effects,” Konya J. Eng. Sci., vol. 8, no. 3, pp. 552–562, 2020.
  • [12] R. Miranda-Colorado, L. T. Aguilar, and J. E. Herrero-Brito, “Reduction of power consumption on quadrotor vehicles via trajectory design and a controller-gains tuning stage,” Aerosp. Sci. Technol., vol. 78, pp. 280–296, 2018.
  • [13] W. Dong, G. Y. Gu, X. Zhu, and H. Ding, “A high-performance flight control approach for quadrotors using a modified active disturbance rejection technique,” Rob. Auton. Syst., vol. 83, pp. 177–187, 2016.
  • [14] R. Miranda-Colorado and L. T. Aguilar, “Robust PID control of quadrotors with power reduction analysis,” ISA Trans., vol. 98, no. xxxx, pp. 47–62, 2020.
  • [15] H. Yao, R. Qin, and X. Chen, “Unmanned aerial vehicle for remote sensing applications - A review,” Remote Sens., vol. 11, no. 12, pp. 1–22, 2019.
  • [16] C. Ben Jabeur and H. Seddik, “Neural networks on-line optimized PID controller with wind gust rejection for a quad-rotor,” Int. Rev. Appl. Sci. Eng., 2021.
  • [17] M. Zareb, W. Nouibat, Y. Bestaoui, R. Ayad, and Y. Bouzid, “Evolutionary Autopilot Design Approach for UAV Quadrotor by Using GA,” Iran. J. Sci. Technol. - Trans. Electr. Eng., vol. 44, no. 1, pp. 347–375, 2020.
  • [18] R. K. Dewangan, A. Shukla, and W. W. Godfrey, “Three dimensional path planning using Grey wolf optimizer for UAVs,” Appl. Intell., vol. 49, no. 6, pp. 2201–2217, 2019.
  • [19] M. N. Shauqee, P. Rajendran, and N. M. Suhadis, “Proportional Double Derivative Linear Quadratic Regulator Controller Using Improvised Grey Wolf Optimization Technique to Control Quadcopter,” Appl. Sci., vol. 11, no. 6, p. 2699, Mar. 2021.
  • [20] Ş. Yıldırım, N. Çabuk, and V. Bakırcıoğlu, “Design and trajectory control of universal drone system,” Measurement, vol. 147, p. 106834, Dec. 2019.
  • [21] V. Bakırcıoğlu, N. Çabuk, and Ş. Yıldırım, “Experimental comparison of the effect of the number of redundant rotors on the fault tolerance performance for the proposed multilayer UAV,” Rob. Auton. Syst., vol. 149, p. 103977, Mar. 2022.
  • [22] M. N. Shauqee, P. Rajendran, and N. M. Suhadis, “An effective proportional-double derivative-linear quadratic regulator controller for quadcopter attitude and altitude control,” Automatika, vol. 62, no. 3–4, pp. 415–433, 2021.
  • [23] M. Karakoyun, A. Ozkis, and H. Kodaz, “A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems,” Appl. Soft Comput. J., vol. 96, p. 106560, 2020.
  • [24] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, Mar. 2014.

Dodecarotor İHA'nın Yörünge Takibi için Bozkurt Optimizasyon Algoritması Temelli Optimal PID Denetleyici Tasarımı

Year 2023, Volume: 11 Issue: 1, 10 - 20, 01.03.2023
https://doi.org/10.36306/konjes.1172687

Abstract

Bu çalışmada, bir dodekarotor İHA'nın minimum yörünge hatası ile yönelimini ve konumunu kontrol etmek için optimal PD kontrolör kazançlarını bulmayı amaçladık. Bu bağlamda, İHA'nın uçuş kontrolünde her bir durum (yuvarlanma, yunuslama, yalpalama, yükseklik) için iç döngüde hız geri beslemesi ve dış döngüde konum geri beslemesi olan kademeli bir PD kontrolör yaklaşımı benimsenmiştir. Sonrasında, yörünge izleme performansı açısından çok etkin olmayan sapma açısı hariç, her durum için sistemin zaman alanı yanıtına ve yörünge izleme hatasına dayalı bir uygunluk fonksiyonu tanımlandı. Tanımlanan uygunluk fonksiyonunu en aza indirerek PD kazanımlarını elde etmek için Bozkurt Optimizasyon Algoritması (GWO) kullanıldı. Aynı zamanda, GWO'dan elde edilen sonuçları kıyaslamak ve sığ bir çözüm alanından kaçınmak için Parçacık Sürü Optimizasyonu (PSO) kullanıldı. Her iki algoritmanın optimizasyon çalışması sonucunda elde edilen PD kontrolör parametreleri sisteme uygulanmış ve sonuçlar birbirleriyle karşılaştırılmıştır. Nihai olarak, her iki algoritma için en iyi sonuçları sağlayan kazançlar birbirleri ile karşılaştırılmış ve sonuçlar zaman alanı sonuçları ve aktüatör giriş düzgünlüğü açısından tartışılmıştır.

Project Number

FBA-2017-7393

References

  • [1] D. Kotarski, Z. Benic, and M. Krznar, “Control Design for Unmanned Aerial Vehicles with Four Rotors,” Interdiscip. Descr. Complex Syst., vol. 14, no. 2, pp. 236–245, 2016.
  • [2] N. Hadi and A. Ramz, “Tuning of PID Controllers for Quadcopter System using Hybrid Memory based Gravitational Search Algorithm – Particle Swarm Optimization,” Int. J. Comput. Appl., vol. 172, no. 4, pp. 9–18, 2017.
  • [3] M. Akhil, M. Krishna Anand, A. Sreekumar, and P. Hithesan, “Simulation of the mathematical model of a quad rotor control system using Matlab Simulink,” Appl. Mech. Mater., vol. 110–116, no. October 2011, pp. 2577–2584, 2012.
  • [4] M. Ayyıldız and K. Çetinkaya, “Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator,” Neural Comput. Appl., vol. 27, no. 4, pp. 825–836, 2016.
  • [5] M. B. Çetinkaya, H. Duran, and T. Hanne, “Performance Comparison of Most Recently Proposed Evolutionary, Swarm Intelligence, and Physics-Based Metaheuristic Algorithms for Retinal Vessel Segmentation,” Math. Probl. Eng., vol. 2022, 2022.
  • [6] M. Mahi, O. K. Baykan, and H. Kodaz, “A new approach based on particle swarm optimization algorithm for solving data allocation problem,” Appl. Soft Comput. J., vol. 62, pp. 571–578, 2018.
  • [7] M. A. Sen and M. Kalyoncu, “Grey wolf optimizer based tuning of a hybrid LQR-PID controller for foot trajectory control of a quadruped robot,” Gazi Univ. J. Sci., vol. 32, no. 2, pp. 674–684, 2019.
  • [8] M. Moussid, A. Sayouti, and H. Medromi, “Dynamic Modeling and Control of a HexaRotor using Linear and Nonlinear Methods,” Int. J. Appl. Inf. Syst., vol. 9, no. 5, pp. 9–17, 2015.
  • [9] S. Li, Y. Wang, J. Tan, and Y. Zheng, “Adaptive RBFNNs/integral sliding mode control for a quadrotor aircraft,” Neurocomputing, vol. 216, pp. 126–134, 2016.
  • [10] Y.-R. Tang, X. Xiao, and Y. Li, “Nonlinear dynamic modeling and hybrid control design with dynamic compensator for a small-scale UAV quadrotor,” Measurement, vol. 109, pp. 51–64, Oct. 2017.
  • [11] Ş. Yıldırım, N. Çabuk, and V. Bakırcıoğlu, “Comparison of Flight Performances of Unmanned Air Vehicle with Six Rotors and Eight Rotors Under Different Disturbance Effects,” Konya J. Eng. Sci., vol. 8, no. 3, pp. 552–562, 2020.
  • [12] R. Miranda-Colorado, L. T. Aguilar, and J. E. Herrero-Brito, “Reduction of power consumption on quadrotor vehicles via trajectory design and a controller-gains tuning stage,” Aerosp. Sci. Technol., vol. 78, pp. 280–296, 2018.
  • [13] W. Dong, G. Y. Gu, X. Zhu, and H. Ding, “A high-performance flight control approach for quadrotors using a modified active disturbance rejection technique,” Rob. Auton. Syst., vol. 83, pp. 177–187, 2016.
  • [14] R. Miranda-Colorado and L. T. Aguilar, “Robust PID control of quadrotors with power reduction analysis,” ISA Trans., vol. 98, no. xxxx, pp. 47–62, 2020.
  • [15] H. Yao, R. Qin, and X. Chen, “Unmanned aerial vehicle for remote sensing applications - A review,” Remote Sens., vol. 11, no. 12, pp. 1–22, 2019.
  • [16] C. Ben Jabeur and H. Seddik, “Neural networks on-line optimized PID controller with wind gust rejection for a quad-rotor,” Int. Rev. Appl. Sci. Eng., 2021.
  • [17] M. Zareb, W. Nouibat, Y. Bestaoui, R. Ayad, and Y. Bouzid, “Evolutionary Autopilot Design Approach for UAV Quadrotor by Using GA,” Iran. J. Sci. Technol. - Trans. Electr. Eng., vol. 44, no. 1, pp. 347–375, 2020.
  • [18] R. K. Dewangan, A. Shukla, and W. W. Godfrey, “Three dimensional path planning using Grey wolf optimizer for UAVs,” Appl. Intell., vol. 49, no. 6, pp. 2201–2217, 2019.
  • [19] M. N. Shauqee, P. Rajendran, and N. M. Suhadis, “Proportional Double Derivative Linear Quadratic Regulator Controller Using Improvised Grey Wolf Optimization Technique to Control Quadcopter,” Appl. Sci., vol. 11, no. 6, p. 2699, Mar. 2021.
  • [20] Ş. Yıldırım, N. Çabuk, and V. Bakırcıoğlu, “Design and trajectory control of universal drone system,” Measurement, vol. 147, p. 106834, Dec. 2019.
  • [21] V. Bakırcıoğlu, N. Çabuk, and Ş. Yıldırım, “Experimental comparison of the effect of the number of redundant rotors on the fault tolerance performance for the proposed multilayer UAV,” Rob. Auton. Syst., vol. 149, p. 103977, Mar. 2022.
  • [22] M. N. Shauqee, P. Rajendran, and N. M. Suhadis, “An effective proportional-double derivative-linear quadratic regulator controller for quadcopter attitude and altitude control,” Automatika, vol. 62, no. 3–4, pp. 415–433, 2021.
  • [23] M. Karakoyun, A. Ozkis, and H. Kodaz, “A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems,” Appl. Soft Comput. J., vol. 96, p. 106560, 2020.
  • [24] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, Mar. 2014.
There are 24 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Şahin Yıldırım 0000-0002-7149-3274

Nihat Çabuk 0000-0002-3668-7591

Veli Bakırcıoğlu 0000-0002-1170-5327

Project Number FBA-2017-7393
Publication Date March 1, 2023
Submission Date September 10, 2022
Acceptance Date November 1, 2022
Published in Issue Year 2023 Volume: 11 Issue: 1

Cite

IEEE Ş. Yıldırım, N. Çabuk, and V. Bakırcıoğlu, “OPTIMAL PID CONTROLLER DESIGN FOR TRAJECTORY TRACKING OF A DODECAROTOR UAV BASED ON GREY WOLF OPTIMIZER”, KONJES, vol. 11, no. 1, pp. 10–20, 2023, doi: 10.36306/konjes.1172687.